System and method for capturing information for conversion into actionable sales leads

ABSTRACT

The present invention relates to business-to-business marketing organizations who participate in lead-generation activities via their company website. More particularly, the invention provides a target lead-generation system and method that targets the right businesses using real-time predictive and behavioral analytics and website traffic data and connects businesses to potential customers and suppliers to drive business revenue. Even more particularly, the invention provides a system and method for real-time cleansing, enriching, and appending of business card data elements such as title, email, and attribute rich company demographic and firmographic data. Additionally, the system appends company-level technology install base data and contact-level data points such as job role, job function, physical location, education, expertise, and social network handles. This contact and company data is appended to the website form and marketing database. The resulting information is then immediately available for use by other systems such as marketing automation systems and CRM systems.

This application is a continuation-in-part of U. S. patent applicationSer. No. 13/447,039 filed on Apr. 13, 2012, which is incorporated byreference herein in its entirety.

BACKGROUND

The present invention relates to business-to-business marketingorganizations who participate in lead-generation activities via theircompany website. More particularly, the invention provides a targetlead-generation system and method that targets the right businessesusing real-time predictive and behavioral analytics and website trafficdata and connects businesses to potential customers and suppliers todrive business revenue. Even more particularly, the invention provides asystem and method for real-time searching and matching of data inputinto website registration forms by website visitors. The system providesfor real-time cleansing, enriching, and appending of standard businesscard data elements such as title, email, and attribute rich companydemographic and firmographic data. Additionally, the system appendscompany-level technology install base data as well as contact-level datapoints such as job role, job function, physical location, education,expertise, and social network handles. All this contact and company datais appending to the website form and to the marketing database. Theresulting information is then immediately available for use by othersystems such as marketing automation systems and CRM systems.

Business to business marketing (“B2B”) includes individuals andorganizations that facilitate the sale of their products and services toother companies or organizations that often resell the products andservices, or use them to support their own operations. Although thedifference between consumer and business marketing may appear obvious,there are many distinguishing features between the two that often resultin substantial differences in practice. For example, B2B marketing mayoften involve shorter and more direct channels of distribution. Whileconsumer marketing often involves large demographic groups targetedthrough mass media and retailers, in B2B marketing the negotiationprocess between the seller and buyer is more personal in nature. MostB2B marketing includes a much more limited portion of promotionalbudgets dedicated to advertising than in consumer marketing. B2Bmarketing and sales is conducted through more direct promotionalefforts, trade journals and sales calls. However, many of the principlesof consumer marketing also apply to B2B marketing, such as definingtarget markets and matching product and service strengths to the definedtarget markets.

One of the more recent promotional endeavors of business marketing isthrough the Internet, involving offered services and products onorganizations' websites. While popular in use, industry research hasshown that of all persons who visit a B2B company's website, only 3% ofvisitors actively identify themselves via forms, thereby leaving 97% ofweb visitors to remain unknown. In addition, of the 3% that announcethemselves, less than 15% fill out a form with complete and accurateinformation. This lack of information makes it very difficult to followup a possible sales lead from a B2B website visitor based oninsufficient information.

In addition to the need for information on businesses visitors to B2Bsales and marketing websites to be provided to the sales and marketingfunnel, quality data is needed by the business for use in marketingautomation systems and customer relationship management (“CRM”) systems.

CRM systems and methods are used by organizations to provide apredictable and organized way for interacting with customers andpotential customers. CRM often includes specially trained personnel andspecial purpose software. It is a combination of policies, processes andstrategies implemented by an organization to unify its customerinteractions and provide a method for tracking customer information. Itoften includes technology for identifying and attracting new andprofitable customers as well as creating better relationships withexisting customers. CRM involves many organizational aspects that relateto one another, including front and back office operations, businessrelationships and interactions, analysis involving target marketing andmarketing strategies, and means for generating metrics for measuring therelative success of various marketing and sales efforts. It is a keycomponent of modern marketing organizations. CRM systems includefirmographic data, which includes characteristics of an organizationoften used for segment market analysis.

Marketing automation systems and methods are used by organizations tocommunicate with prospects and customers and automate many marketingcommunication tasks. Marketing automation often includes speciallytrained personnel and special purpose software. Whereas a CRM system isoften leveraged as a database for the sales organization, a marketingautomation solution is mostly leveraged as a database for the marketingorganization. Furthermore, there is typically a link that exists betweena marketing automation solution and a CRM system. Marketing automationis often leveraged to communicate with customers and prospects viaemail, track and report on campaign responses, profile the quality andsales-readiness of leads generated by marketing programs, prioritizewhich leads are passed to members of a sales team, and to automateongoing communications to prospects not yet ready to purchase.

Therefore, quality data from customers is needed to be able to leverageand exploit marketing automation systems and CRM systems. One of themajor drawbacks of many of the B2B sales and marketing productsavailable today is the lack of data quality when generating existing andnew customer contact data. A problem for B2B marketers is that a largegap exists between the need to capture rich individual and businessdemographic and firmographic information from new leads that completeonline registration forms, and the need to keep registration forms shortto reduce form abandonment. B2B marketers need accurate andcomprehensive data to route leads to the right sales representative,segment & target their marketing efforts, and perform other leadprioritization and communication activities. At the same time, marketersalso need to increase as much as possible the number of website visitorsfrom their marketing programs who fill out their registration forms.Marketers have been forced to choose between shortening theirregistration forms to drive higher conversions (registrations), orrequire the registrant to complete too many fields, resulting inincreased registration abandon rates and an increased averagecost-per-lead.

Other challenges with registration forms are that the data that resultafter a customer/visitor enters information is often false or inaccuratedata. This may be that a visitor makes errors providing its data, or maynot know the correct answers required by the registration form. Anotherkey challenge is identifying the true leads from spambots, automatedcomputer programs designed to assist in the sending of spam that crawlInternet websites looking for registration forms and automatically enterfake data. Once a spambot finds a form, it parses and analyzes the form.The spambot then may fill them with unwanted information, hyperlinks andvisuals that are intended to attract a target audience. This is oftendone to increase the number of hyperlinks to a particular web site, toboost its search engine ranking.

Addressing all of these above-mentioned challenges has required manualefforts on the part of marketing organizations to sift through all ofthe forms submissions and attempt to correct inaccurate firmographicdetails and eliminate the false records. Manual methods of correctinginputted form data can be time consuming and can result in lost customerleads. If the marketing organization takes too long to qualify theleads, potential new customers may have already identified and electedto purchase the product or service being offered from another vendorwhich responds more quickly.

B2B marketing is at the beginning of a new era that heavily relies onthe tight integration of inbound and outbound marketing initiatives. Asthis transformation happens, marketers need help increasing theirconversions and accelerating their leads through the marketing and salesfunnel for faster revenue growth. However, a huge gap exists between theneed to capture rich business firmographic information from new leads,and the need to keep registration forms short. Marketers are forced tomake a lose, lose decision: shorten their registration forms to drivehigher conversions, but go without critical information, or require theregistrant to complete too many fields, resulting in increased rates ofabandonment of the web form entry by the registrant resulting in ahigher cost-per-lead. Rich data attributes are required by their salesand marketing systems, as well as the critical customer and prospectinsight needed to better manage opportunities through the sales andmarketing funnels.

An ideal solution is one that provides as close to 100% accurate companyinformation for a business visitor to a website. Such a solution is nottrivial since less than 3% of website visitors are identified. Drawbacksof previous solutions include outdated and inaccurate information andthe lack of a simple and cost-effective way to objectively andanalytically identify and connect visitors with their companies so as tobe able to target such companies for outbound marketing.

A solution is required that enables companies to accelerate conversionsthrough their sales and marketing funnels by reducing the amount ofinformation that a company requests on the web forms while appending thedata needed to run their business behind the scenes and in real time. Indoing this, customers see a sharp reduction in web-form abandonmentleading to a significant increase in the percentage of visitors whoprogress through the registration cycle.

The solution presented herein is a multi-pronged approach that canleverage visitor entered/selected information, and/or IP addressidentification of the visitor, and/or a process that automaticallymatches individual and company level information to the individual andcompany of a website visitor leveraging complex searching and matchingalgorithms and a master data management platform. The result is ashortened registration form that delivers, invisibly to the websiteregistrant, all of the information a marketer needs to run theirbusiness. Companies that have implemented the solution provided hereincan achieve a more than a 50% increase in web-form completion andconversion rates and achieve more than a 30% reduction in their cost perlead.

It is also important to offer such solutions as Software as a service(“SaaS”). SaaS is a model of software deployment where a providerlicenses a software application to customers for use as a service ondemand. SaaS vendors may host an application on their own web servers ordownload the application to the customer device, disabling it after useor after an on-demand contract expires. By sharing end user licenses andon-demand use, investment in server hardware may be reduced or shiftedto a SaaS provider. SaaS is usually associated with business softwareand is considered to be a low cost method for businesses to obtainrights to use software as needed rather than licensing all hardwaredevices with all applications. On-demand licensing provides the benefitsof commercially licensed use without the associated complexity andpotentially high initial cost of equipping each hardware device withsoftware applications that are only used occasionally.

SUMMARY

The present invention is a system and method to selectively identify andtarget marketing activities to the set of companies from which webvisitors are originating but whose visitors do not actively identifythemselves to the sponsoring website company. It performs as a Softwareas a service (SAAS) deployment.

Features of the described application for identifying website visitorsincludes the means of a small code fragment that can be embedded in aclient's website for collecting and sending and trackingnon-personally-identifiable information about passive web visitors bythe present invention. As this passive web visitor data accumulates, theclient can then view this data as well as other publically availablecompany information, set up business rules to view and filter companiesbased on a number of visits, pages visited and firmographic criteria,such as industry, revenue range and employee population size.

The present invention is also a targeted lead generation system, whichuses a combination of analytical applications to assist B2B marketers inidentifying ideal markets and companies within those markets to targettheir lead generation efforts. The B2B marketing economy in 2005 wasseventy seven billion dollars with almost two thirds of that amountspent in field marketing and demand generation. The top issue forcompanies trying to market to other businesses is reaching the correctbuyer decision maker, often called a target. Billions of dollars arewasted annually in unsuccessful marketing attempts to reach the righttarget. Despite annual spending in 2005 of twenty seven billion dollarson demand generation activities such as email marketing, webinars,search marking and online advertisements, B2B marketers still experiencezero to three percent conversion rates that is being able to reach theright target. Other related problems involve inability to measuremarketing results, improving lead quality and generating more leads.

The present invention addresses the B2B marketing data gap in part byproviding high quality data for B2B demand generation. A typical supplychain view of B2B marketing involves lead generation and marketing andsales force automation as part of customer relationship management whichalso includes customer service and support. It provides intelligence toautomate and streamline lead generation and marketing and sales forceautomation.

The present invention solves the marketing problems of targeting theright companies with marketing and sales campaigns, targeting the rightroles of likely decision makers, identifying the right segments of themarket where a company is currently winning customers, identifying thedeal velocity of opportunities through the sales funnel, identifyingpatterns in the opportunities in the sales funnel, identifying companieswith the same characteristics as other companies that the business isselling to and justifying marketing spending by measuring results. Itsolves these problems with analytics and algorithms that target theright businesses and the right roles of likely decision makers andbuyers within those businesses. Included is a custom developed workflowengine that leverages a company's internal data and third party data.Data services for targeted lead generation include custom data creationservices using a role-base model of the decision maker, marketing leads,a discovery data inference engine and workflow to drive advantagedeconomics of data services and a data refresh and update databaseservice for in-house leads and customer contact data. Software servicesfor marketing decisions include targeting campaigns based on win andsales funnel analysis, leveraging web site visits and converting theminto targeted leads and profiling of in-house data to surgically fixdata quality issues. In summary, the present invention helps businessestarget the right companies to sell to, reach the right person withinthose companies and connect to those persons in the right way mostlikely to generate a positive response.

The core of these marketing service applications is a platform formarketing and sales contact management that provides increased dataquality. These include a SaaS-based data services technology platformthat provides the following features.

Real-time Predictive Analytics—Automatically recommends new targetbusinesses based on “cluster patterns” identified via real-time analysisof client wins data and sales pipeline data within CRM systems and/orweb visitor profiles.

An innovative Role-based data model for contact records, which canpinpoint accuracy of the right contact. This Role-based data modelemploys cutting-edge Web 3.0 semantic data principles to provide aunique capability for identifying the right person based on the Role ofan individual aligned with a company's product/solution valueproposition.

An on-demand contact discovery model based on intelligent heuristics inwhich contact data is generated only upon client request, resulting infresh, 100% accurate contacts that drive performance increases of20×-30× for marketing campaigns.

A real-time query engine technology component that will enables queriesacross social network destinations and augment the traditional contactdata attributes, such as name, title, phone, email, with social mediapresence information. This “query for quorum” approach not only servesas an additional tier of contact validation but will also assist clientsin formulating social marketing strategies to reach their prospects byidentifying if and where those prospects are participating in socialnetworking.

Providing Real-Time Firmographic Information Based on Minimal Web FormInput Embodiment

An alternate embodiment of the present system and method solves theproblem of not having quality data from website visitors/customer maynot accurately identify themselves to the sponsoring website company.The present system and method provides sponsoring companies withreal-time attribute rich lead and company firmographic data based onminimal web form input data entered by their website visitor and whosevisitors may not have accurately identified themselves on the web form.The present invention addresses the B2B marketing data gap by providinghigh quality data for B2B demand generation. It solves the marketingproblems of targeting the right companies with marketing and salescampaigns by allowing its users to selectively identify and targetmarketing activities to the set of companies associated with the webvisitors. The resulting computer system and method may be deployed as aSoftware as a Service (SAAS).

Features of the described application for providing lead and companyfirmographic data include a relatively small code fragment or softwareclient that is embedded in a sponsoring company's online web form. Thissoftware client utilizes the website visitors' responses to companybased input criteria to perform internet protocol (IP)address-to-company searches, fuzzy criteria searches, and/or analyticalcriteria matches based on statistical scoring algorithms. Thesereal-time searching and matching modules each utilize combinations ofmultiple input parameters to provide highly accurate results.Standardized company firmographic data, such as physical address,industry, revenue range, and employee size are appended in real-time tothe web form as the result of a successful search or match allowing theresults to be immediately available to customer marketing automationsystems, CRM systems or both upon initial data entry. An availablemodule allows for real-time visitor email address verification. Anavailable module allows for CASS verification of physical addressinformation wherein the geographic attributes of each contact arevalidated against third party services to ensure accuracy anddeliverability for direct mail.

The core of the described application includes a SaaS-based data servicetechnology platform that provides the following modules and associatedfunctionality:

Application Client with Automated Workflow—

This application client provides a configurable software applicationclient which effectively eliminates a significant portion of customcoding required by the sponsoring company for a successful deployment.This allows for the non-technical staff to have a working implementationin place extremely fast, decreasing time to market and reducingimplementation cost. In addition, the application client coordinates theactions of the modules of the system and method described herein andtheir interaction with at sponsoring company's existing web form. As webvisitor data accumulates, the application client allows for the viewingof this data as well as other publically available and proprietarycompany information, provides the ability set up business rules to viewand filter companies based on a number of visits, pages visited andfirmographic criteria, such as industry, revenue range and employeepopulation size.

Real-Time Reverse IP Address Searches—

This application provides the functionality for detecting the IP addressof a web form visitor, reverse mapping that IP address to a company, andthen providing that company's firmographic data in real-time to theform. This allows the sponsoring company to auto-detect the visitorscompany and auto-populate the form data with or without directinteraction from the visitor.

Real-Time Company Searches—

This application provides the functionality for utilizing a webvisitor's company data entered in a form to be used in a multi-stagefuzzy search conducted at a SaaS provider's Real-Time Search Database.The attributes of the visitor's company are used to fuzzy searchcommercial databases of company information. Such commercial databasesmay be located locally to the firmographic analytical system or may bean external commercial database, or both. Combinations of multiple inputvariables can be used all of which are assigned unique precedence andweight values to be utilized by the fuzzy-search algorithm. The initialsearch is highly targeted. If no results are returned after this initialsearch, subsequent searches use fewer and fewer company attributes for abroader search until a result set is found. When using the ApplicationClient, results of a company are presented to the visitor in aninteractive select list allowing the visitor to select the exact companythey are employed by. This interactive select list is configurableallowing multiple display options including an inline drop-down modewhich displays results with each key-stroke of the visitor and a modalconfirmation dialog box mode which displays results once the visitorcompletes the form. Upon a visitor selecting a company presented in theselect list, the selected company's firmographic data is provided to theform where it updates hidden fields created so that the system andmethod receives this real-time search data.

Real-Time Company Matching—

This application provides the functionality for utilizing a webvisitor's company data entered in a form to be analyzed remotely at theSaaS provider's master data management (MDM) database utilizing amatching engine where matching is conducted via statistical scoringalgorithms against a commercial database of company information.Combinations of multiple input variables can be used which are allassigned unique precedence/weight values to be utilized by the MDMmatching algorithm. Company firmographic data from the best match(es) isprovided to the form in real-time along with a score which indicates theconfidence level of the match(es). This operation can be completelyhidden from the web visitor.

Real-Time Email Address Validation—

This application provides the functionality for utilizing an intelligentscoring-based proprietary set of Internet research techniques to improveupon existing commodity methods, which generates a validation score foreach email address.

Real-Time CASS Address Verification—

This application provides the functionality for allowing the geographicattributes of each delivered company to be validated against third partyservices to ensure accuracy and deliverability of direct mail. The CASSsoftware function corrects, matches and standardizes street addresses.

An embodiment of the present invention comprises a real-time softwareapplication method hosted on a server for capturing information forconversion into actionable sales leads. It comprises collecting websitevisitor information in real-time via a communication network when awebsite visitor accesses a web form on a third-party company website.Visitor information is imported in real-time to a firmographicanalytical application running in real-time on the server and comprisingthe steps of: mapping a visitor's IP address to a name of a visitorcompany owner of the IP address; matching web form data entered data bythe visitor to visitor company owner firmographic attributes andinformation in a commercial database; validating visitor email addressand returning a validation score; validating geographic addressattributes of the visitor company owner; aggregating and sending visitorcompany owner firmographic information to the visitor's browser to bedisplayed on the visitor's web form; and sending and appending visitorcompany owner firmographic data to the visitor's web form.

BRIEF DESCRIPTION OF DRAWINGS

These and other features, aspects and advantages of the presentinvention will become better understood with regard to the followingdescription, appended claims, and accompanying drawings wherein:

FIG. 1 illustrates a functional block diagram of an embodiment of thepresent invention;

FIG. 2 is an example illustration of a Resource Description Frameworkmodel for role-based contacts;

FIG. 3 is a depiction of the confluence of a client request and thevalidated contacts database;

FIG. 4 is an illustration of a Resource Description Framework model forcompany attributes;

FIGS. 5A, 5B, 5C and 5D are flow diagrams of workflow with adaptivesteering where “direct hits’ or “correlated” contacts are not found;

FIG. 6 is a flow diagram of an embodiment of a method for collecting andanalyzing visitors of companies' websites;

FIG. 7 is a flow diagram of an embodiment of a method for identifyingand associating information from web services with information from aclient's customer relationship management system;

FIG. 8 depicts a client user interface for analyzing client wins data;

FIG. 9 depicts a client user interface for analyzing client funnel data;

FIG. 10 depicts a client user interface for analyzing client fastestwins data by industry, annual revenue and employee population size;

FIG. 11 depicts a client user interface dashboard view for proactivelytargeting lead generation;

FIG. 12 depicts a client user interface detailed view for proactivelytargeting lead generation.

FIG. 13 depicts a computer system and network suitable for implementingthe system and method of providing real time firmographic informationbased on minimal web form input;

FIG. 14 is a block diagram showing the firmographic analytic applicationand its major interfaces;

FIGS. 15A and 15B are flow charts of the firmographic analyticalapplication processing;

FIG. 16, a flow chart of the reverse IP address search function;

FIG. 17 shows a flow diagram of the real time company search functionprocessing;

FIG. 18 shows a block diagram of the real-time address validationfunction;

FIG. 19 shows a real-time Coding Accuracy Support System (CASS)application function;

FIG. 20 is an exemplary depiction of a set of firmographic data that isthe output of the process described in FIGS. 15A and 15B;

FIG. 21 is an exemplary depiction of the output of the real-time companymatching function described FIGS. 13, 15A, 15B and 17;

FIG. 22 is an exemplary depiction of the output of the real-time companymatching function and real-time company search function described inFIGS. 13, 15A, 15B and 17.

FIG. 23 shows a flow diagram of the real time business card dataelements and company level technology install base data matchingfunctions processing;

FIG. 24 shows a block diagram of the real time business card dataelements validation function;

FIG. 25 shows a company level technology install base data verificationmodule function;

FIGS. 26A and 26B are flow charts of the firmographic analyticalapplication processing;

FIG. 27 shows a table of exemplary inferred company data;

FIG. 28 shows a table of exemplary contact enrichment data; and

FIG. 29 shows a table of exemplary inferred technology install data.

DETAILED DESCRIPTION OF INVENTION

Turning to FIG. 1, FIG. 1 illustrates a functional block diagram 100 ofan embodiment of the real time analytics application 110, web visitorapplication 135, and the data services platform 115. It provides atargeted lead-generation system that targets the right businesses usingwebsite traffic data for reaching the right business buying person viarole-based contact data and connects businesses to potential customersand suppliers to drive business revenue.

Real-Time Analytics

In FIG. 1, a Customer Relationship Management (CRM) System 105 is ahosted software application as a service (SaaS) instance of a type ofsales force automation software including but not limited tosalesforce.com software. This CRM application 105 is used by the clientas a system of record for tracking sales and marketing data, such asleads, contacts, accounts, opportunities and client wins. Client CRMdata 105 is accessed by the real time analytics application 110 forcreating a list of companies within which contacts and sales leads aredesired. The real time analytics application 110 includes a set ofself-service analytics tools that enable clients to create targetcompany lists based on objective criteria, such as a client's CRMsystem. A more detailed description of this real time analyticsapplication 110 is discussed below in relation to FIG. 7.

Web Visitor Application

FIG. 1 also includes a web visitor application 135 that receives datafrom client website visitor information from a code segment embedded inthe client website 130. This web visitor application 135 is provided forclients who wish to focus their contact discovery efforts on companiesthat are frequenting their corporate website 130. This application 135employs reverse-IP address lookup technology to identify, from an IPaddress of a client website visitor, the name of the company to whichthe IP address belongs. From there, a multi-stage matching algorithm isused to augment each reverse-mapped company name with firmographicinformation. A client user can then sort, filter and prune through thefull list of visiting companies to identify a target set that matchestheir needs and provide that list to the data services platform as atarget list. A more detailed description of this web visitor application135 is discussed below in relation to FIG. 6.

It should be noted that at times clients will have a prepared list ofcompanies 160 or are able to express the firmographic characteristics ofthe types of companies they are intending to target. In these cases, thecompanies or parameters are input to a list building tool provided as apart of the data services platform functionality.

Role-Based Contact

As shown in FIG. 1, target company data from the real time analyticsapplication 110, the web visitor application 135, and the pre-identifiedcompanies 160 may be provided to the role-based contacts component 165.With a target company list identified, the next step is selecting theright role description by the role-based contacts component 165, ormodifying one from the role catalog 165. A role description is anEnglish-language definition of job function that makes a target contactideal for the client's marketing requirements. To illustrate, roles cantypically be described by completing the following sentence:

We are targeting the person responsible for _(——————).

It is often the case that this role description is augmented withsupplementary bounding information around suggested titles anddepartments to specifically seek and/or avoid. An example of this moresophisticated description would be:

We are targeting the person responsible for _(——————). This person istypically in the _(——————) or _(——————) department and may carry thetitle of _(——————) or _(——————). This person must explicitly not residein the _(——————) or _(——————) department and must not bear the title of_(——————) or _(——————).

This vernacular is often foreign to marketers whose innate response whenquestioned about who they are targeting is a title-based response, suchas “the VP of Sales” or “Director of IT”. The role catalog 165 assistsclients in reshaping their thinking around roles instead of titles,which are poor predictors of the job functions a person actuallyperforms. The role catalog 165 is a unique hybrid-Resource DescriptionFramework (“RDF”) 140, a semantic data representation of storedinformation that contains mappings of titles to roles. A more detaileddescription of this RDF model 140 for role-based contacts 165, 170 isdiscussed below in relation to FIG. 2.

Company Targeting

Once the target company list 110, 135, 160 and roles 165 have beenidentified, the contact discovery process is then initiated and severaltechnology components are employed to maximize the leverage of existinginformation around titles, roles, companies and contacts to drivediscovery costs downward. These components are company targeting andsteering component 170 and the proximity heuristics engine component175. The company targeting and steering component 170 is described ingreater detail below in relation to FIG. 3. This component 170 steers alist of target companies by searching for companies that intersectbetween the client-defined criteria set and companies previouslyresearched that are contained in the validated contact database 120.Where contacts match a target company and a role criteria, the result isconsidered a “direct hit”.

Proximity Heuristics

The proximity heuristics engine component 175 relies on an underlyingdata model of the data services platform 115 that is an intelligentmodel that draws upon the Classifier and Statistical Learning methods ofartificial intelligence. This model increases accuracy and relevance,i.e. “gets smarter”, as more data is created within it. Informationabout all dimensions of the data produced, such as titles, roles,companies, contacts, are leveraged for present and future contactproduction, refresh or verification cost advantages. When a target roleenters the system at a discovery initiation point, the system employs aheuristic statistical distribution model to match, correlate andprovision existing contacts that directly match or are in closeproximity to a desired role as determined either by existing role ortitle. Where existing contacts directly match or are in close proximityto a desired role within a defined threshold, the match is considered tobe “correlated”. The proximity heuristics engine component 175 isdescribed in greater detail below in relation to FIG. 4.

Automated Workflow

As noted above, where contacts match a target company and a rolecriterion, the result is considered a “direct hit”, and where existingcontacts directly match or are in close proximity to a desired rolewithin a defined threshold, the match is considered to be “correlated”.For the remainder set of target companies where “direct hit” or“correlated” contacts were not found, the data services platform 115provides an automated workflow 145 that guides researchers through theexplicit set of process steps and transitions required to find orrefresh the right role-based contacts. The automated workflow component145 is described in greater detail below in relation to FIG. 5. Thereal-time feedback component 185 is a non-automated function of the dataservices platform 115.

Validation and Quality Assurance Technologies

As contacts are successfully discovered, the data services platform 115employs a host of processes and automated quality assurance technologies190 delivered within the contact manufacturing line to ensure that acontact is, in fact, the right contact and that the information that hasbeen provided about the contact is accurate. Every contact that isreleased to clients undergoes the following automated verification andvalidation processes:

Email address validation—the system employs an intelligent scoring-basedproprietary set of Internet research techniques to improve upon existingcommodity methods, which generates a score for each email address in therange of [0 . . . 5]. Only contacts with email addresses scoring a 4 or5 rating will be released to the client.

CASS address verification—the geographic attributes of each contact arevalidated against third party services to ensure accuracy anddeliverability for direct mail performance.

Search engines and other Internet resources, such as social networksLinkedIn, FaceBook and others are used to further verify that thecontact exists at the stated company and that they fulfill the targetrole description.

Event logging produces forensics data enabling QA resources to validatethat the appropriate steps were taken to discover and validate contactdata and role applicability.

In-stream title analysis ensures contacts with titles that fall out ofdesired specification do not proceed through the workflow.

Dual-stage quality processes ensure role attribution and physicalcontact data are correct for each contact through VOIP call recordinganalysis, optimized web search tools and logging.

Taken together, these processes are effective in ensuring delivery of ahigh quality contact. The data services platform includes a real-timesocial network query engine component 180 to further these qualityassurance methods by interrogating social network destinations to testfor contact presence. The contacts 150 identified as a result of theautomated workflow component 145 and the automated quality assurancecomponent 190 are stored in the contacts database 120 of the dataservices platform 115 and in the clients' CRM systems.

Reporting and Instrumentation

The Data Services Platform requires a low skill barrier to usage andproductivity. Contact discovery projects are delegated, monitored,tracked and measured throughout the process lifecycle by ProjectManagers. Researchers are provided with a rigid process flow thatnavigates them through the various stages of contact discovery andprovides various means of assistance throughout the process.

The system is instrumented pervasively for reporting and analysis acrossseveral dimensions including quality, milestone achievement,productivity, performance, and capacity and revenue forecasting. ProjectManagers and Executives have access to real-time business intelligencethat provides for facilities such as:

Researcher efficiency grading, enabling managers to monitor, guide andtake steps to improve individual researcher performance

Project and Agent level KPIs, enabling managers to guide projects tocompletion faster with less error.

Stage-level cycle-time analysis, illustrating areas of the‘manufacturing line’ which need staffing modifications to ensure fasterthroughput.

Role penetration analysis, enabling determination of Role definitionperformance

Assignment and reallocation of researchers to activities aligned withtheir skill levels

Dynamic adjustment of capacity for active researchers within and acrossresearch centers

Production capability and planning, enabling managers to scale resourceneeds to match production needs and capabilities.

Revenue forecasting, enabling managers to make intelligent planningdecisions in real-time

Reject analysis to surface error cluster trends, enabling in-processchanges to project definitions and attainment of velocity and qualitygoals while reducing effort and opportunity waste.

Productivity hotspots, enabling managers to scale down researchresources during slow periods and anticipate potential performancebottle necks.

Turning to FIG. 2, FIG. 2 is an example illustration of a ResourceDescription Framework model and role catalog 200 for role-basedcontacts. Contact Y is first identified 210 and has an IT role 220, anIT hardware role 230 and an IT storage management role 240. The rolecatalog 165 contains mappings for thousands of unique roles, spanningunique titles across a universe of over 600,000 contacts in the contactdatabase 120. This catalog is text-indexed for search purposes and isused to illustrate the role paradigm to clients and prompt them toeither select an existing role or modify an existing role.

In cases where neither a match nor template can be found that is similarenough to the client's role, the client can create a new role which willbe used for their contact discovery purposes, thus extending the rolecatalog for future use. Once the target company list and roles have beenidentified, the contact discovery process is then initiated and severaltechnology components are employed to maximize the leverage of existinginformation around titles, roles, companies and contacts to drivediscovery costs downward. These components include the Company ListSteering and the Proximity Heuristics Engine.

Turning to FIG. 3, FIG. 3 is a depiction 300 of the confluence 320 of aclient request 310 and a validated contacts database group 330. In caseswhere the clients either have firmographic criteria that describes theset of companies they wish to target or are open to supplementing anexplicit list of target companies with additional companies matching aset of firmographic criteria, the data services platform 115 is able to“steer” the resulting target list of companies by searching forcompanies that intersect between the client-defined criteria set andcompanies previously researched, and therefore contain existingcontacts. This advantages the discovery process, at a minimum, bysurfacing a set of companies for which has known good contacts thatmatch the client's target role description. In the optimal case,contacts that match both the target company and Role criteria arerendered, resulting in a “direct hit”. In the event of a “direct hit”where the contact validation date is beyond a stated aging threshold of90 days, the data services platform 115 will not automatically provisionthat contact directly to the client. Instead, the data services platformwill conduct a faster, lower cost refresh process to verify that thecontact data and role responsibility is still current before shipping itto the client.

Turning to FIG. 4, FIG. 4 is an illustration 400 of a ResourceDescription Framework model for company attributes and company liststeering 170. In the example of FIG. 4, Company X 410 uses a CRM system420, provided by Siebel 430, a version of Enterprise 440 Services andSupport 450. The underlying data model of the data services platform isan intelligent model that draws upon the Classifier and StatisticalLearning methods of artificial intelligence. This model increasesaccuracy and relevance (i.e. “gets smarter”) as more data is createdwithin it. Information about all dimensions of the data produced by thedata services platform, including titles, roles, companies, contacts,which are leveraged for present and future contact production, refreshor verification cost advantages. When a target role enters the system atthe discovery initiation point, the system employs a heuristicstatistical distribution model to match, correlate and provisionexisting contacts that directly match or are in close proximity to adesired role as determined either by existing role or title. If thenumber of times Title_(Tx) occurs for Role_(Ry)>=Threshold_(Dn), theengine infers that Title_(Tx) is a likely candidate to match the targetRole_(Ry). Depending on the depth of information around the targettitles and roles, the system may derive several such titles for a givenrequest. In circumstances where the specific role for a target companyis not found but contacts exist, the correlation engine can determine ifany of those contacts perform or are likely to perform the desired role.This engine can correlate role-to-title relationships even when the listof target companies varies significantly in size or revenue.

The hybrid-Resource Description Framework (RDF) data model also supportstagging of company attributes outside of the stock firmographiccriteria. Information about technologies deployed within companies andother internal characteristics are persisted and stored in a hybrid-RDFformat for advanced company data mining. The heuristics engine can notonly predict likely titles for desired roles, but also identify whichcompanies are most likely to employ people with those desired roles.Capturing the knowledge of relationships between roles and companiesdrives more precise targeting and selection of companies.

Turning to FIGS. 5A, 5B, 5C and 5D, they show a flow diagram of workflow500 with adaptive steering where “direct hits” or “correlated” contactsare not found. Where “direct hit” or “correlated” contacts were notfound, the Data Services Platform provides an automated workflow thatguides researchers through the explicit set of process steps andtransitions required to find or refresh the right role-based contacts.Depicted is a receipt of contacts 510 where “direct hits” or“correlated” contacts are not found. It shows the steps of the workflowprocess 500 that transform the received contacts 510 into a Hard FullDiscover 520, a Full Discover 530, an Assisted Discover 540, anAdvantaged Discover 550, a Stale Correlated Hit 560 (over 90 days sincerefreshed), a Correlated Hit 570, a Stale Direct Hit 580 (over 90 dayssince refreshed), and a Direct Hit 590. To assist researchers in theirefforts to locate the target role-based contacts, the system once againleverages the Proximity Heuristics Engine 175 to query third partycontact data sources 125 for contacts at the target company, at aminimum, and, where possible, likely to be in proximity to the desiredcontact based on title. As the discovery process operates, the systemprovides real-time feedback mechanisms to researchers that indicatewhich characteristics of their delivered contacts (ex. titles,departments) are resulting in higher approval rates. This enablesresearchers with in-process discovery items to hone their efforts andadapt their discovery tactics to produce higher yields and higherquality contacts that align to the clients' requirements.

Turning to FIG. 6, FIG. 6 is a flow diagram of an embodiment of a method600 for collecting and analyzing visitors of companies' websites. Theweb visitor application 600 (135 in FIG. 1) provides for enabling theselective identification and targeting of marketing activities to theset of companies from which web visitors are originating but whosevisitors do not actively identify themselves to the company. The clientis provided a small code fragment 610 to be embedded in the client'swebsite that will capture and send non-personal visitor information to adata capture service provided by the web visitor application (135 inFIG. 1). Once the code fragment is in place, as visitors arrive on thepages of the client's website that have been instrumented with the codefragment, information about the visitor is transmitted to the webvisitor application 620. The information that is transmitted is theentire set of fields and values provided via the HTTP Request Header asspecified via the HTTP protocol specification and does not include anypersonally identifiable information about the visitor, such as thevisitor's first and last name, phone number or email address. Thisinformation is stored within a database accessible by the web visitorapplication 630. On a periodic basis, a scheduled program automaticallyprocesses all the web visit data for the current accumulation period andresolves collected IP addresses from the website visit information intothe names of the business entities from which the visit originated 640.If no business entity name can be found for a given IP address or the IPaddress resolves to an Internet Service Provider (ISP), such asroadrunner.com, aol.com, yahoo.com, the visit record is excluded fromrendering by the user interface. After the business entity name has beenresolved, an attempt to match each business entity name against adatabase containing company names and firmographic information, such asindustry, revenue and employee population size, is performed 650. Forbusiness entities that are matched successfully, the source record isattributed with the corresponding industry, revenue and employeepopulation size values 660. If a match cannot be found, the businessentity record is excluded from rendering by the user interface. Usenetan IP address, the system can render the name of the company and thecompany's firmographic attributes which can then be used by the systemto identify similar companies with like attributes. The system can thenfind the right people to target within those companies along with theircontact information. This process and functionality continues andrepeats for the duration that the code fragment 610 remains on theclient website. To retrieve the processed and attributed visitor data,the client is provided with a web-based user interface 670 to accessstored visitor data originating from the code fragment as describedpreviously. This user interface enables the user to select a timeframeof visit data to analyze and renders the visit data accordingly. Thedata is rendered in two views; one graphical depiction showingconcentrations of visitor data by company headquarter location andindustry, and one non-graphical table view of the visitor data and itsassociated attributes. Users of the Customer Relationship Management(CRM) systems that automate sales automation such as salesforce.com arealso presented with the option to perform a proxy login to theirrespective sales force automation account (see 105 in FIG. 1) to enablethe system to perform an analysis of which visiting companies arepresent within the user's sales force automation CRM database.

Turning to FIG. 7, FIG. 7 is a flow diagram of an embodiment of a methodfor identifying and associating information from web services 700 withinformation from a client's customer relationship management system. Thepurpose of this contact discovery process is to create a list ofcompanies within which contacts are desired. The data services platformprovides a set of self-service analytics tools that enable clients tocreate target company lists based on objective criteria, such asclient's CRM system. This analysis assumes very little data integritywithin the user's CRM system and only the names of the companiesidentified in the user's CRM system as either clients or activeprospects are used to initiate the segmentation process. It is throughthe means of a multi-stage fuzzy matching algorithm that the applicationmatches the user's company names to fully-attributed company records inthe master database. The results of this analysis are then aggregatedand the user is presented their “cluster patterns”, or firmographicdescriptions of companies which the user's customers and/or prospectsare found to be in highest concentration. Once these cluster patternsare ascertained, the application then queries the database to surfacethe number of other companies that match the identified cluster patternsthat the user does not currently have resident in their CRM system, thuspresenting the remaining total addressable market available for aparticular cluster pattern. This list of companies derived from thisprocess then serves as the input list of target companies within whichthe contact discovery processes is performed. The process comprisesimporting client contact data from the client's CRM system 710 andmatching the imported data with firmographic data 720. The client isprovided with a user interface to view client wins data 730 (see FIG.8), and allows the client the ability to filter information, selectrecords and obtain reports 740. A multi-stage fuzzy matching algorithmis used to match customer company names to a fully-attributed companyrecords database and find cluster patterns 750. The user interfaceprovides information for targeting sales and marketing efforts 760 andallows a user to query the application database to identify otherunidentified companies that match the found cluster patterns 770.

Table 1, shown below, depicts the ability of a user to select a set ofcompanies or the entire list of companies for examination. The user canalso filter the list of companies by industry, revenue, employeepopulation, location or any combination thereof. The user may also electto export the active list, which results in the creation of atab-delimited text file on a server containing all respectiveinformation for each selected company. This file can then be harvestedby a human employee and either processed in the context of a discoverdata services project or simply made available to the user via emailattachment.

TABLE 1 Company Industry Revenue Employees Location Visits In CRM C1 I1R1 E1 HQL1 N1 True/false/- C2 I2 R2 E2 HQL2 N2 True/false/- . . . . . .. . . . . . . . . . . . . . . Cn In Rn En HQLn Nn True/false/-

Turning to FIG. 8 and FIG. 9, FIG. 8 and FIG. 9 depict a client userinterface for analyzing client wins data, where FIG. 8 depicts selectionof wins analysis 810 and FIG. 9 depicts selection of funnel analysis910. This SAAS application analyzes, augments and reports on “in-funnel”sales data, turning static information into actionable campaigns basedon current deal flow. It allows a company to determine if they aremarketing to the right companies, identify trends in a sales funnel thata company is not capitalizing on, identify the kinds of leads that movethrough the sales funnel the fastest and generate the most revenue, allof which are common questions marketers ask themselves as they aredeveloping lead generation programs. The information that results fromthis application allows marketing and sales teams to agree on winningtarget markets and focused lead generation efforts at other companiesthat match this profile. In addition to highlighting winning marketsegments, the application allows marketing and sales teams to look intotheir sales funnel and identify current trends. By analyzingopportunities in the sales funnel in real time, marketers can adjustprograms on-the-fly to help keep deals moving to close.

The application provides a snapshot of a company's winning marketsegments and the activities that contributed to these wins.

As shown in FIG. 8, a client wins analysis allows a client to highlightwinning market segments, identify how many more companies have similarprofiles to winning segment, highlight new client wins with the shortestsales cycles, pinpoint the kinds of companies that move through thesales funnel the fastest, and allows marketing and sales teams are ableto better target outreach efforts. FIG. 9 illustrates how a client mayuse a funnel sales analysis to understand patterns within opportunitiesin the active sales funnel, better forecast new client wins, focusefforts on industries that are driving the most revenue for thebusiness, and create or adjust marketing programs to help moveopportunities to close. These figures provides identification of the setof companies that match the desired profile, and the system shown inFIG. 1 provides additional data services for role-based contactdiscovery within these new target companies. The combination of theapplication shown in FIG. 8 and FIG. 9 with the data services, allowsmarketing and sales teams to ensure they are reaching out to not onlythe right businesses but also the right decision making roles withinthose businesses.

Turning to FIG. 10, FIG. 10 depicts a client user interface 1000 foranalyzing client fastest wins data 1010 by industry, annual revenue andemployee population size. This feature enables greater efficiencies isincreasing the velocity of wins.

Turning to FIG. 11 and FIG. 12, FIG. 11 depicts a client user interface1100 dashboard view 1110 for proactively targeting lead generation andFIG. 12 depicts a client user interface 1200 detailed view 1210 forproactively targeting lead generation. These user interfaces provide forsetting up business rules to select, filter, review, prioritize andpotentially score visitors based on the companies that are visiting,number of visits, pages visited and time on website and proactivelytargets unannounced web visitor. They provide reporting on where inboundvisitors are coming from, such as search engines, blogs, emailcampaigns, as well as where the companies are geographically located.They also enable profiles of top visitors by industry and appends theserecords with industry verticals, SIC codes, revenue and employeepopulation size. With this data, a company can better target unannouncedvisiting companies but also get contacts from companies with similarprofiles. Once the companies that are visiting the website unannouncedhave been identified, the system shown in FIG. 1 provides data servicesfor role-based contact discovery within these new target companies.

FIG. 13 depicts a computer system and network 1300 suitable forimplementing the system and method of providing real time firmographicinformation. A server computer 1305 includes an operating system 1310for controlling the overall operation of the server 1305 which mayconnect through a communications network 1315 to a company's website1320, a company's CRM system 1325 and, optionally to local computers1330 with a user interface device. The company's website 1320 contains acustomer or prospect (also known herein as a “visitor”) web form 1340that contains an embedded software code fragment or client application1345 embedded within the user's browser 1335. A visitor visits thecompany's website 1320 and opens a company web form 1340 that containsthe embedded software client application 1345. The user's browser 1335will load the JavaScript that runs the embedded application 1345. Theserver computer 1305 hosts a software as a service (SaaS) application1345 comprising the real time firmographic analytic application 1350.The firmographic analytic application 1350 comprises multiple softwareapplications including an automated workflow and application client1355, a real-time reverse IP address search application 1365, areal-time company search application 1365, a real-time company matchingapplication 1370, a real-time email address validation application 1375,a real-time Coding Accuracy Support System (CASS) application 1380 andvarious other real-time analytics applications 1385 which could includeheuristic engines, other known analytics or analytics as describedherein. The firmographic analytic application 1350 also may include theembedded application client 1345 that resides within the company's webform 1340. A real-time search database 1385 allows for real-timesearching of a visitor's company data using search algorithms by thefirmographic analytic application 1350. The real-time search database1385 is comprised of business records retrieved, developed and cleansedusing records from within one or more external commercial databasesources 1395. The real-time company matching application 1370 comprisesa matching engine with statistical algorithms that matches company webform 1340 data entered by the visitor to the company's website 1320 witha master data management database 190 that contains commercial businesscompany data.

The automated workflow application 1355 provides a configurable workflowthat allows a company user to configure the firmographic analyticapplication 1350 off-line without the need for significant customercoding. The real-time company search function 1365 provides a module forutilizing a web visitor's company data and other data entered in a webform 1340 to be used in a multi-stage fuzzy search conducted using thereal-time search database 1385. The attributes of the visitor's companyare used to search the real-time search database 1385 which containscommercial company information that has been retrieved, developed andverified using external commercial databases 1395. The real-time companysearch utilizes fuzzy search matching that matches a pattern rather thanrequiring an exact match, although exact match and other types of searchalgorithms could also be used. Combinations of multiple input variablescan be used which are all assigned unique precedence/weight values asconfigured by the automated workflow and application client function1355 can be utilized by the fuzzy-search algorithm. The initialreal-time company search 1365 may be highly targeted to one or moremultiple input variables/attributes. The search results comprise anumber of companies having the highest weighted scores on the closenessof the match to the search criteria. The actual number of companyresults to be returned is configurable by the automated workflow andapplication client. If no results are found in this initial search,subsequent searches use fewer and fewer company attributes for a broadersearch until a result set is found. When using the firmographicanalytical application 1350, company search results may be presented tothe visitor in an interactive select list allowing the visitor to selecttheir exact company. This interactive select list is configurableallowing multiple display options including an inline drop-down modewhich displays results with each key-stroke of the visitor and a modalconfirmation dialog box mode which displays results once the visitorcompletes the form. Upon a visitor selecting a company presented in theselect list, the selected company's firmographic data is provided to thecompany web form 1340 where it updates hidden fields created so that thereal-time company search 1365 data results are available either to thevisitor or to the company that owns the company website 1320. Thereal-time business card data elements matching function 1360 provides amodule for utilizing a web visitor's company data and other data enteredin a web form 1340 to be used in a multi-stage fuzzy search conductedusing the real-time search database 1385 and marketing database 1371.The attributes of the visitor's company are used to search the real-timesearch database 1385 and the marketing database 1371 which containsbusiness card elements such as title, email, and attribute rich companydemographic and firmographic data that comprise commercial companyinformation that has been retrieved, developed and verified usingexternal commercial databases 1395, web searching and marketingdatabases 1371. The real-time company search utilizes fuzzy searchmatching that matches a pattern rather than requiring an exact match,although exact match and other types of search algorithms could also beused. Combinations of multiple input variables can be used which are allassigned unique precedence/weight values as configured by the automatedworkflow and application client function 1355 can be utilized by thefuzzy-search algorithm. The initial real-time company search 1365 may behighly targeted to one or more multiple input variables/attributes suchas title, email, and attribute rich company demographic and firmographicdata. Using the real-time company technology install database searchfunction 1372, the system appends company-level technology install basedata 1372 as well as contact-level data points such as job role, jobfunction, physical location, education, expertise, and social networkhandles. The search results comprise a number of companies having thehighest weighted scores on the closeness of the match to the searchcriteria. The actual number of company results to be returned isconfigurable by the automated workflow and application client. If noresults are found in this initial search, subsequent searches use fewerand fewer company attributes for a broader search until a result set isfound. When using the firmographic analytical application 1350, companysearch results may be presented to the visitor in an interactive selectlist allowing the visitor to select their exact company. Thisinteractive select list is configurable allowing multiple displayoptions including an inline drop-down mode which displays results witheach key-stroke of the visitor and a modal confirmation dialog box modewhich displays results once the visitor completes the form. The selectedbusiness card information such as title, email and attribute richcompany demographic and firmographic data is appended to the websiteform 1340 and to the marketing database 1371. It may also be appended torecords in the company install base data 1372 where it updates fieldscreated so that the real-time business card data elements matchingresults 1360 are available to the web form 1340 and in the marketingdatabase 1371. FIG. 27 shows exemplary inferred company data. FIG. 28shows exemplary contact enrichment data with exemplary data fields andsample data. FIG. 29 shows exemplary inferred technology install datawith exemplary data fields and sample data.

FIG. 14 is a block diagram showing the firmographic analytic applicationand its major interfaces 1400. When a visitor accesses a company webform 1405, the firmographic analytical application 1410 (which may behosted as a SaaS solution running on a remotely located server) isaccessed and data is appended 1415 to a web form 1405 as furtherdisclosed herein. Standardized company firmographic data, such asphysical address, industry, revenue range, employee size and the likeare appended in real-time to the web form 1405 as the result of asuccessful search or match with data in a real-time search database FIG.13, 1385 and a master data management database FIG. 13, 1390, allowingthe results upon initial data entry to be immediately available tocompany customer's marketing automation systems 1420 and CRM systems1425.

FIGS. 15A and 15B are flow charts of the firmographic analyticalapplication processing 1500. When a visitor accesses a company websiteform 1505 is activated and the real-time reverse IP address searchfunction 1510 is activated.

Turning now to FIG. 16, a flow chart of the reverse IP address searchfunction 1600, the reverse IP address search function 1605 provides amodule for detecting the IP address of a visitor accessing the companyweb form 1610 (FIG. 13, 1340) at a company website FIG. 13, 1320,reverse mapping that IP address to the visitor's company by doing an IPcheck and company match 1615 including searching a real-time searchdatabase 1620 having company IP addresses. Processing is controlled bythe automated workflow and application control function 1625.

Turning back to FIG. 15A, the results of the reverse IP address searchand the resulting company firmographic information having the particularIP address that corresponds to the web visitor who is entering data onthat company web form 1520 is then used in real-time to automaticallypopulate the company web form and append firmographic data to hidden andnon-hidden fields 1515. This allows the company whose website is beingvisited FIG. 13, 1320 to auto-detect the visitor's company andauto-populate the company web form 1520 with data with or without directinteraction from the visitor. As part of the real-time reverse IPfunction 1510 internet searches are performed 1525 may occur.

As the visitor is filling out the web form 1535, the real-time companysearch function 1540 is activated to perform real-time search ofdatabases 1530. Alternatively, the automated workflow (at the company'soption) can perform the real-time search company search function 1540after the user submits the webs form 1545.

FIG. 17 shows a flow diagram of the real time company search functionprocessing 1700. The real-time company search function 1705 isactivated. Input data 1 through n 1705, 1710, 1715, 1720 that is inputto a company web form by a visitor to the company website FIG. 15A, 1535is each given a respective precedence and weight setting 1725, 1730,1735, 1740 that is provided by the automated workflow function FIG. 13,1355 when a user from the company sets up the firmographic analyticapplication FIG. 13, 1350. Alternatively, if the user has not set up theautomated workflow FIG. 13, 1355 within the firmographic analyticapplication FIG. 13, 1350, then the precedence and weight settings 1725,1730, 1735, 1740 will be default settings. The input data 1705, 1710,1715, 1720 is used to perform real-time company searches of thereal-time search database and master data management database 506 usingsearch algorithms 1707, 1708 for real-time database searches. The outputdata A and B 1709, 1710 is then returned to the automated workflow andapplication control function 1745 which activates other modules tocontinue and complete the appending of firmographic data to the webform.

Turning back to FIG. 15A, when the user activates the web form submitbutton 1545 the web form validation processes 1550 continue on FIG. 15B.Depending upon the automated workflow settings, when the user activatesthe web form button 1545, the real-time company search function 1540 canalso be performed. A real-time email address validation module 1555validates the email address by accessing the real-time search database1530 and returns a validation score. The real-time email addressvalidation module 1555 provides a module that utilizes an intelligentscoring-based proprietary set of Internet search techniques that providefor improved search results over commonly used Internet searchtechniques and generates a score for each email address that representsa measure of the validity of the respective email address.

The real-time CASS address verification function 1565 providesfunctionality that allows the address 1570 geographic attributes of eachcompany to be validated against the real-time search database 1530 orother third party services to ensure accuracy and deliverability fordirect mail.

The results of the processing describe in the firmographic analyticapplication 1500 is presented as an interactive select list 1580 thatmay be displayed to the user in the web form as an interactive selectlist of companies 1580. This interactive select list is configurableallowing multiple display options including an inline drop-down modewhich displays results with each key-stroke of the visitor and a modalconfirmation dialog box mode which displays results once the visitorcompletes the form. If the visitor selects one of the companiespresented to the user as part of or ancillary to the web form 1585, theprocessing continues in step 1590. If the visitor does not select one ofthe companies presented to the visitor 1586 then a master datamanagement function algorithm 1587 is activated and a master datamanagement database is searched 1588 for a firmographic data best match.In step 1590, then the user selected firmographic data 1585 or themaster data management function firmographic data is appended 1590 tothe web form in hidden and unhidden fields. The form submission andappending process is now complete and the data is available for releasedto and use by applicable systems such as marketing automation systems,CRM systems or local databases 1595.

FIG. 18 shows a block diagram of the real-time address validationfunction 1805. A proprietary analysis and score of the validity of theemail address is provided utilizing an intelligent scoring-basedproprietary set of internet research techniques 1815. The score isreturned and used in the company web form 1820. Processing is controlledby the automated workflow and application control function 1810.

FIG. 19 shows a real-time Coding Accuracy Support System (CASS)application function 1900. The CASS module 1905 validates and confirmsthe validity of the physical address 1910 either input on the web form1920 or obtained from third party databases or services. The geographicattributes of the web form information 1920 are validated against thirdparty services to ensure accuracy and deliverability for direct mail.Processing is controlled by the automated workflow and applicationcontrol function 1915.

FIG. 20 is an exemplary depiction of a set of firmographic data 2000that is the output of the process described in FIGS. 15A and 15B. Thedepiction in FIG. 20 represents data to be placed into a web form'shidden fields.

FIG. 21 is an exemplary depiction of the output of the real-time companymatching function 2100 (described above in FIGS. 13, 15A, 15B and 17).It shows exemplary firmographic data that is the output of theprocessing described in FIGS. 15A, 15B and 17 and includes data from thevisitor's selected company or resulting from an MDM matching algorithmand search. It may also represent data that may have been manuallyentered by the visitor. The depiction in FIG. 21 represents data that tobe placed into a web form's hidden fields in real-time. Such informationmay be selected from the group consisting of: name, email address,company name, company address, company URL, number of employees, companyannual revenue, SIC code data, NAICS data and a data confidence level.The data confidence level will comprise a confidence level that is basedon whether the company data was entered by the visitor; if the matchalgorithms have returned a high score and therefore considered is a goodmatch; if the match algorithms have returned a score that indicates thatthe match is a good match, but may have had fewer data fields upon whichto conduct the match but results in a score that is still high enough tocall a match; or a match failure because the match algorithms hasreturned a low confidence level and is recommended that any matchresults not be considered accurate.

FIG. 22 is an exemplary depiction of the output of the real-time companymatching function and real-time company search function 2200 (describedabove in FIGS. 13, 15A, 15B and 17). It shows exemplary firmographicdata that is the output of the processing described in FIGS. 15A, 15Band 17. The depiction in FIG. 22 represents data that includes companyand company affiliate information and related hierarchical company datato be placed into a web form's hidden fields.

FIG. 23 shows a flow diagram of the business card data elements andcompany level technology install base data searching and matchingfunctions processing 2300 that may occur in real-time. The business carddata elements and company level technology install base data searchingand matching functions 2305 are activated. Input data 1 through n 2305,2310, 2315, 2320 that is input to a company web form by a visitor to thecompany website FIG. 26A, is each given a respective precedence andweight setting 2325, 2330, 2335, 2340 that is provided by the automatedworkflow function FIG. 13, 1355 when a user from a company sets up thefirmographic analytic application FIG. 13, 1350. Alternatively, if theuser has not set up the automated workflow FIG. 13, 1355 within thefirmographic analytic application FIG. 13, 1350, then the precedence andweight settings 2325, 2330, 2335, 2340 will be default settings. Theinput data 2305, 2310, 2315, 2320 is used to perform real-time companysearches of the real-time search database and master data managementdatabase 2306 using search algorithms 2307, 2308 for real-time databasesearches. The output data A and B 2309, 2310 is then returned to theautomated workflow and application control function 2345 which activatesother modules to continue and complete the appending of firmographicdata to the web form.

Turning to FIG. 26A, when the user activates the web form submit button2645 the business card data elements and web form data processes 2650continue on FIG. 26B. Depending upon the automated workflow settings,when the user activates the web form button 2645, the real-time companysearch function 2640 can also be performed. A real-time email addressvalidation module 2655 validates the email address by accessing thereal-time search database 2630 and returns a validation score. Thereal-time email address validation module 2655 provides a module thatutilizes an intelligent scoring-based proprietary set of Internet searchtechniques that provide for improved search results over commonly usedInternet search techniques and generates a score for each email addressthat represents a measure of the validity of the respective emailaddress.

The real-time CASS address verification function 2665 providesfunctionality that allows the address 2670 geographic attributes of eachcompany to be validated against the real-time search database 2630 orother third party services to ensure accuracy and deliverability fordirect mail.

The real-time company level technology install data base function 2691provides functionality that allows company-level technology install basedata with contact-level data points such as job role, job function,physical location, education, expertise, and social network handles 2692to be validated and appended to the web form and to a marketing database2695.

The results of the processing describe in the firmographic analyticapplication 2600 is presented as an interactive select list 2680 thatmay be displayed to the user in the web form as an interactive selectlist of companies 2680. This interactive select list is configurableallowing multiple display options including an inline drop-down modewhich displays results with each key-stroke of the visitor and a modalconfirmation dialog box mode which displays results once the visitorcompletes the form. If the visitor selects one of the companiespresented to the user as part of or ancillary to the web form 2685, theprocessing continues in step 2690. If the visitor does not select one ofthe companies presented to the visitor 2686 then a master datamanagement function algorithm 2687 is activated and a master datamanagement database is searched 2688 for a firmographic data best match.In step 2690, then the user selected firmographic data 2685 or themaster data management function firmographic data is appended 2690 tothe web form in hidden and unhidden fields. The form submission andappending process is now complete and the data is available for releasedto and use by applicable systems such as marketing automation systems,CRM systems or local databases 2695.

FIG. 24 shows a block diagram of the real-time business card dataelements validation function 2405. A proprietary analysis and score ofthe validity of the business card data elements such as title, email andattribute rich company demographic and firmographic data is providedutilizing an intelligent scoring-based proprietary set of internetresearch techniques 2415. A score is returned and used to validate datathat will be appended to in the web form 2420 containing business carddata elements and to the marketing database 2425. Processing iscontrolled by the automated workflow and application control function2410.

FIG. 25 shows a real-time company level technology install base dataapplication function 2500. The company level technology install basedata module 2505 validates and confirms the validity of the job role,job function, physical location, education, expertise, and socialnetwork handles 2510 either input on the web form 2520 or obtained fromthird party databases or services. The geographic attributes of the webform information 2520 are validated against third party services toensure accuracy and deliverability for direct mail. A proprietaryanalysis and score of the validity of the company level technologyinstall base data such as job role, job function, physical location,education, expertise, and social network handles is provided utilizingan intelligent scoring-based proprietary set of internet researchtechniques 2530. A score is returned and used to validate datacontaining company level technology install base data such as job role,job function, physical location, education, expertise, and socialnetwork handles that will be appended to in the web form 2520 and to themarketing database 2425. Processing is controlled by the automatedworkflow and application control function 2515.

FIG. 27 2700 shows exemplary inferred company data with exemplary datafields and sample data.

FIG. 28 2800 shows exemplary contact enrichment data with exemplary datafields and sample data.

FIG. 29 2900 shows exemplary inferred technology install data withexemplary data fields and sample data.

Although the present invention has been described in detail withreference to certain preferred embodiments, it should be apparent thatmodifications and adaptations to those embodiments might occur topersons skilled in the art without departing from the spirit and scopeof the present invention.

The invention claimed is:
 1. A computer implemented method fordiscovering, validating and outputting contact information usingreversed internet protocol mapping and heuristic artificialintelligence, the computer implemented method comprising: embedding, bya computer system, a tracking code fragment into a website;automatically detecting, by the computer system, from the tracking codefragment, internet protocol addresses of visitors accessing the website;reverse mapping, by the computer system, the internet protocol addressesof the visitors accessing the website to identify target companies towhich the internet protocol addresses belong; matching, by a weighted,multi-stage matching algorithm, executed by the computer system, each ofthe target companies with firmographic records stored in a masterdatabase; aggregating, by the computer system, the firmographic recordsinto cluster patterns; sorting, filtering and pruning, by the computersystem, the target companies based on the firmographic records;detecting, by a proximity heuristic statistical distribution learningmodel, executed by the computer system, when a title of a contact in onethe target companies correlates to a desired targeted role in excess ofa minimum frequency threshold, creating validated contact and companydata by: validating the title of the contact by real-time queryingonline social networks and calculating a validation score indicating aconfidence level for the title of the contact; validating the contact bytaking the contacts validated title and using firmographic records andquerying online social network for the group consisting contact jobrole, contact job function, contact physical location, contact educationand contact social network handles of the validated contact, in eachcase assigning a validation score indicating a confidence level for thecontact job role, contact job function, contact physical location,contact education and contact social network handles validating, by thecomputer system, an email address of the contact by real-time queryingonline social networks and calculating a validation score indicating aconfidence level for the email address of the contact; validating, bythe computer system, a geographic address of the contact against thirdparty services via a coding accuracy support system that corrects,matches, standardizes and confirms validity of the geographic address;outputting, by the computer system, the validated contact and validatedcompany data, on a user interface; and appending the validated contactand company data to the website form displayed on a user interface andappending the validated contact and company data to a data store.
 2. Themethod of claim 1, further comprising auto-populating, by the computersystem, a form of the website with the firmographic records.
 3. Themethod of claim 2 wherein the auto-populating the form occurs withoutdirect interaction from the visitors.
 4. The method of claim 1, whereinthe firmographic records comprise one or more of: an industry of thetarget companies, and a revenue range of the target companies.
 5. Themethod of claim 1, further comprising identifying when the firmographicrecords were entered by an automated computer program.
 6. The method ofclaim 5, wherein the automated computer program is a spambot.
 7. Themethod of claim 1 wherein the validation score comprises values between0 and
 5. 8. A system for discovering, validating and outputting contactinformation using reversed internet protocol mapping and heuristicartificial intelligence, the system comprising a server computerconfigured for: embedding, a tracking code fragment into a website;automatically detecting, from the tracking code fragment, internetprotocol addresses of visitors accessing the website; reverse mapping,the internet protocol addresses of the visitors accessing the website toidentify target companies to which the internet protocol addressesbelong; matching, by a weighted, multi-stage matching algorithm, each ofthe target companies with firmographic records stored in a masterdatabase; aggregating, the firmographic records into cluster patterns;sorting, filtering and pruning, the target companies based on thefirmographic records; detecting, by a proximity heuristic statisticaldistribution learning model, when a title of a contact in one the targetcompanies correlates to a desired targeted role in excess of a minimumfrequency threshold; validating, an email address of the contact byreal-time querying online social networks and calculating a validationscore indicating a confidence level for the email address of thecontact; creating validated contact and company data by: validating thetitle of the contact by real-time querying online social networks andcalculating a validation score indicating a confidence level for thetitle of the contact; validating the contact by taking the contactsvalidated title and using firmographic records and querying onlinesocial network for the group consisting contact job role, contact jobfunction, contact physical location; contact education and contactsocial network handles of the validated contact, in each case assigninga validation score indicating a confidence level for the contact jobrole, contact job function, contact physical location, contact educationand contact social network handles validating, by the computer system,an email address of the contact by real-time querying online socialnetworks and calculating a validation score indicating a confidencelevel for the email address of the contact; validating, a geographicaddress of the contact against third party services provided by a codingaccuracy support system that corrects, matches, standardizes andconfirms validity of the geographic address; outputting, the validatedcontact and validate company data, on a user interface; and appendingthe validated contact and company data to the website form displayed ona user interface and appending the validated contact and company data toa data store.
 9. The system of claim 8, wherein the server computer isfurther configured for auto-populating, a form of the website with thefirmographic records.
 10. The system of claim 9 wherein theauto-populating the form occurs without direct interaction from thevisitors.
 11. The system of claim 8, wherein the firmographic recordscomprise one or more of: an industry of the target companies, and arevenue range of the target companies.
 12. The system of claim 8,wherein the validation score comprises values between 0 and 5.